Multimodal Integration of MRI and Genetic Information for Glioblastoma Survival Prediction

Glioblastoma (GBM) remains a brain tumor with extremely poor prognosis, necessitating precise survival prediction to guide personalized treatment planning. Each MRI modality highlights distinct biological features of GBM, while genetic information provides crucial context for understanding tumor and progression. This complementary information offers a more comprehensive understanding of GBM. However, existing survival prediction methods, using either statistical approaches or deep learning models, often fail to capture the intricate relationships between multimodal MRI data and genetic markers, causing significant challenges to effective integration. To address these challenges, we propose a novel framework that integrates multimodal MRI and genetic information through a tailored fusion approach reflecting the distinct biological characteristics of each modality. Our method integrates multimodal MRI data and genetic information through a modality-aware fusion approach, which preserves modality-specific features and adjusts feature representations to integrate genetic information. This design achieves superior performance by modeling modality-specific features and cross-modal interactions, while incorporating genetic information to refine the feature representation for survival prediction. As a result, this framework supports clinicians in making informed, personalized treatment decisions, ultimately enhancing patient outcomes.